If you have Snowflake or are considering it, now is the time to think about your ETL for Snowflake. This blog post describes the advantages of real-time ETL and how it increases the value gained from Snowflake implementations.

Withinstant elasticity, high-performance, and secure data sharing across multiple clouds, Snowflake has become highly in-demand for its cloud-based data warehouse offering. As organizations adopt Snowflake for business-critical workloads, they also need to look for a modern data integration approach. A streaming ETL for Snowflake approach loads data to Snowflake from diverse sources – such as transactional databases, security systems’ logs, and IoT sensors/devices – in real time, while simultaneously meeting scalability, latency, security, and reliability requirements.

There are several advantages of using a streaming data integration solution such as Striim to enable real-time ETL for Snowflake. Here are the Top 5:

Using Striim’s real-time data synchronization capabilities, businesses have the option to implement a phased migration to Snowflake from existing on-prem or cloud-based data warehouses. As such, there is no downtime for the legacy environment, and risks are minimized by allowing for extensive testing of the new Snowflake environment.

Before loading the data to Snowflake with sub-second latency, Striim allows users to perform in-line transformations, including denormalization, filtering, enrichment and masking, using a SQL-based language. In-flight data processing reduces the time needed for data preparation as it delivers the data in a consumable form.

In-flight transformation also enables a simplified and scalable data architecture that has many related benefits including:

Minimizing ETL workloads by performing transformations while data is in motion

Optimizing data storage by filtering out unnecessary data

Enabling end-to-end recoverability and full resiliency without needing to handle many different components and network hops across these components